Biometrics refers to automated techniques for establishing the identity of a human subject by uniquely detecting, determining, or recognizing subject-specific characteristics that fall within an essentially universal or population-based category of intrinsic human characteristics, features, or traits. Various types of biometric systems exist, including systems directed to uniquely identifying a subject based upon fingerprint, palm print, retinal pattern, or facial feature data.
Technological advancements have significantly improved the feasibility of automated face recognition systems. In general, face recognition systems capture one or more facial images of a subject under consideration; extract subject-specific facial features from the captured images; and determine a best match between the extracted facial features and reference facial feature data corresponding to a number of subjects, including the subject under consideration.
The appearance of the same subject's face can dramatically change under different lighting conditions, particularly with respect to the angles at which one or more light sources are disposed and/or the illumination intensities of such light sources relative to the subject's face. For instance, a given subject's face can appear dramatically different between low, generally low, or mixed light conditions (e.g., a dimly lit room, or an outdoor scene under a shaded tree) and bright light conditions (e.g., a snowy winter scene, in which the subject may appear silhouetted).
Face recognition systems have historically suffered from the inability to provide accurate, consistent, or reliable recognition results under varying lighting conditions. While certain face recognition techniques have demonstrated high recognition accuracy under highly controlled or predictable lighting conditions (e.g., typical indoor environments such as home or office settings), recognition accuracy substantially decreases when such techniques are applied across a range of uncontrolled or less-predictable real-world lighting situations (e.g., outdoor environments). More particularly, different or unpredictable real world lighting conditions can result in poor or unreliable face recognition system performance as a result of increased rates of false rejection or false acceptance.
As described in “Illumination Invariant Face Recognition Using Near-Infrared Images” by Li et al., IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 4, April 2007, one approach for improving face recognition accuracy across variable lighting conditions relies upon the generation and application of relatively low intensity near-infrared (NIR) illumination of a known intensity to a subject's face, the capture of NIR facial image data, and the processing of the NIR facial image data in accordance with a facial recognition algorithm. However, Li et al. admit that this approach is not suitable for use in outdoor environments, because ambient outdoor light includes a strong near-infrared component.
In “Specific Sensors for Face Recognition,” Advances in Biometrics, Lecture Notes in Computer Science, 2005, Volume 3832/2005, 47-54 (Springer-Verlag), Hizem et al. describe a NIR-based face recognition technique in which an array of infrared LEDs is used to capture facial images. Unfortunately, the technique described by Hizem et al. results in a bulky and/or cost inefficient device that fails to provide suitable facial recognition performance across a sufficiently wide range of real-world lighting conditions. Additionally, the technique described by Hizem et al. may not be able to reliably determine whether a captured facial image corresponds to a live person rather than, for example, a printed picture of a person.
It is therefore desirable to provide a solution to address at least one of the foregoing problems associated with existing approaches for face recognition or authentication.